Abstract
Feature selection plays a critical role in data mining, driven by increasing feature dimensionality
in target problems. In this paper, we propose a
new criterion for discriminative feature selection,
worst-case discriminative feature selection (WDFS). Unlike Fisher Score and other methods based
on the discriminative criteria considering the overall (or average) separation of data, WDFS adopts a new perspective called worst-case view which
arguably is more suitable for classification applications. Specifically, WDFS directly maximizes the
ratio of the minimum of between-class variance of
all class pairs over the maximum of within-class
variance, and thus it duly considers the separation
of all classes. Otherwise, we take a greedy strategy by finding one feature at a time, but it is very
easy to implement and effective. Moreover, we utilize the correlation between features to help reduce
the redundancy, and then WDFS is extended to uncorrelated WDFS (UWDFS). To evaluate the effectiveness of the proposed algorithm, we conduct
classification experiments on many real data sets.
In the experiment, we respectively use the original
features and the score vectors of features over all
class pairs to calculate the correlation coefficients,
and analyze the experimental results in these two
ways. Experimental results demonstrate the effectiveness of WDFS and UWDFS